Hi Linda - I'm estimating a GMM with MLR for pain occurrence as a binary variable across 7 months. I'm getting conceptually very interesting results, but in trying to finalize the 3-class and especially the 4-class solutions, I keep getting errors that result from (at least partly), classes that keep shifting in order. One problem is that the L and Q slope estimates are sometimes astoundingly big (and I don't know how they are scaled). For example, in the most recent problematic model, the problem class has S= 539.098 and Q= -269.345 and the program fixed Q (threshold is 1.723). But what is that S?!? But S is large for another class also (8.117). Basically, the primary problem class has no pain at baseline, 100% with pain at month 2, then a steep decline in the probability of pain over months 3-7. Any help with these estimates and how to keep the classes in order would be great! Thanks, Bruce

So the 2nd class in my 4-C model starts at close to a P=0, then goes to P=1 at T2, then returns to P=0 by T4. They represent a meaningful class. (1) How can I model the group? I can't fix the S and Q to e(logit) = infinity or minus infinity. (2) Can I estimate a mixture model for these data with a piecewise approach? If so, what would the model look like, taking off from